Deformable Model Based Marked Controlled Liver Ct-Scan Image Segmentation

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2017 by IJETT Journal
Volume-46 Number-4
Year of Publication : 2017
Authors : B.Manasa, L.Akhila, G.Shivaji Babu
DOI :  10.14445/22315381/IJETT-V46P239

Citation 

B.Manasa, L.Akhila, G.Shivaji Babu "Deformable Model Based Marked Controlled Liver Ct-Scan Image Segmentation", International Journal of Engineering Trends and Technology (IJETT), V46(4),226-230 April 2017. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group

Abstract
Liver is a gland that plays a major role in metabolism with numerous functions in the human body, including regulation of glycogen storage, decomposition of red blood cells, plasma protein synthesis, hormone production, and detoxification. The diagnosis of liver disease is made by liver function tests, groups of blood tests that can readily show the extent of liver damage. If infection is suspected, then other serological tests will be carried out. Sometimes, an ultrasound or a CT scan is needed to produce an image of the liver. Physical examination of the liver can only reveal its size and any tenderness, and some form of imaging will also be needed. Computerized liver tumor segmentation on contrast-enhanced method is proposed for CT images. It is a challenging problem due to the great diversity of shape, intensity and texture. Deformable models such a 3D active surface a previously existing 2D active contour mode. GVF based active contour techniques are used to segmented the liver in the CT scan image and detects the fatty liver and identify the various problems. Pre- Processing is done by adaptive bilateral filter which remove noise improves contrast and preserves edges. A marker controlled active contour method is proposed for liver segmentation. The performance of the proposed method is evaluated.

 References

[1] V.Dey, Y.Zhang, M.Zhong., 2010.A Review on image segmentation techniques.
[2] L. Rusko, G. Bekes, M. Fidrich, Automatic segmentation of the liver from multiand single-phase contrast-enhanced CT images, Med. Image Anal. 13 (2009) 871–882.
[3] D. Smeets, D. Loeckx, B. Stijnen, B. De Dobbelaer, D. Vandermeulen, P. Suetens, Semi-automatic level set segmentation of liver tumors combining a spiralscanning technique with supervised fuzzy pixel classification, Med. Image Anal. 14 (2010) 13–20.
[4] J. Lee, N. Kim, H. Lee, J.B. Seo, H.J. Won, Y.M. Shin, et al., Efficient liver segmentation using a level-set method with optimal detection of the initial liver boundary from level-set speed images, Comput. Methods Progr. Biomed. 88 (2007) 26–38.
[5] Y. Boykov, G. Funka-Lea, Graph cuts and efficient N-D image segmentation, Int. J. Comput. Vis. 70 (2006) 109–131.
[6] G. Chen, L. Gu, L. Qian, J. Xu, An improved level set for liver segmentation and perfusion analysis in MRIs, IEEE Trans.Inf. Technol. Biomed. 13 (2009) 94–103.
[7] E. Göçeri, M.N. Gürcan, O. Dicle, Fully automated liver segmentation from SPIR image series, Comput. Biol. Med. 53(2014) 265–278.
[8] Kass M., Witkin A., Terzopoulos. D.: Snakes: Active Contour Models, in Proceedings of 1st International Conference on Computer Vision, London, 1987, pp. 259-267
[9] Marek J., Demjénová E., Tomori Z., Janá?ek J., Zolotová I., Valle F., Favre M., Dietler G.: Interactive Measurement and Characterization of DNA Molecules by Analysis of AFM images, Cytometry Part A, Volume 63A, Issue 2, March 2005, pp. 87-93, Wiley-Liss, Inc.
[10] Demjénová E., Zolotová I., Tomori Z.: Interactive Segmentation of Fibrelike Objects, in Proceedings of 2nd Slovakian-Hungarian Joint Symposium on Applied Machine Intelligence, SAMI 2004, Her?any, Slovakia, January 16-17, 2004, pp. 259-268, ISBN 963 7154 23 X.
[11].B.Sridhar, K.V.V.S.Reddy, A.Mallikarjuna Prasad “Clustered micro calcification detection based on the Texture feature extraction using Adaptive bilateral filter, mathematical morphology" i-manager`s Journal on Software Engineering.Vol:8 No:4 April – June 2014 pp. 18-25

Keywords
Liver analysis, bilateral filtering, active contour techniques, dice coefficients.